With high-dimensional data, which of the following can become unreliable?

In the context of high-dimensional data, all aspects stated in the options can become unreliable due to the complexities and challenges that arise when the number of predictors exceeds the number of observations.

R^2, which measures the proportion of variance explained by the model, can be misleading in high-dimensional settings. As more variables are added to the model, R^2 will generally increase, even if those variables do not contribute meaningful information. This can create an illusion of a better fit that does not truly reflect the model's predictive power.

The fitted equation also faces issues in high dimensions. As the number of predictors increases, the model can become overly complex, leading to overfitting where it captures noise rather than the underlying data structure. This results in a fitted equation that does not generalize well to new, unseen data.

Confidence intervals for regression coefficients can become unreliable as well when working with high-dimensional data. With many predictors, the estimates for coefficients may become unstable, leading to wide confidence intervals. This can make it difficult to determine the true significance and effect size of each predictor variable, thereby complicating inference.

Because of these challenges, it is accurate to say that in high-dimensional contexts, all the listed metrics and assessments—R^2, the fitted